Day 159 — Day 168 Project Kickoff: Image Classifier for a Complex Dataset
Today at a Glance
Set up a production-grade image classification project targeting CIFAR-100 (100 classes, 60,000 images)
Conduct exploratory data analysis to understand class distribution, sample quality, and inter-class similarity
Design the full system architecture, from raw pixel ingestion through augmentation pipelines, model selection, and evaluation strategy
Lay down the project scaffolding that Days 160-168 will build on top of
Why This Matters
Every production computer vision system at scale — Google Lens classifying 200+ plant species, Tesla’s object detection distinguishing pedestrians from cyclists, Pinterest’s visual search mapping images to 10,000+ product categories — started exactly here: someone had to decide the architecture before writing a single line of model code.
The decisions you make on Day 1 of a project, dataset splits, augmentation strategy, whether to train from scratch or fine-tune, have cascading effects on every downstream component. A model that reaches 92% accuracy on the training set but ships with a leaky train/test split is worse than useless in production. This project week teaches you to think like an ML engineer, not just a model trainer.
Agenda
Understanding CIFAR-100 and why complex datasets break naive CNN approaches
Project architecture: pipeline layers, data flow, and evaluation contracts
Exploratory data analysis: class imbalance, visual difficulty, superclass groupings
Transfer learning decision framework: when to fine-tune vs. train from scratch
Project directory scaffolding and reproducibility practices
